[英]How to run ARIMA model for each column of a dataframe?
I´m predicting the prices for a good in 5 regions. 我正在预测5个地区的商品价格。 The data is organized as a Pandas data frame.
数据被组织为熊猫数据框。
When I run autoARIMA for each column apart, it is fine (eg, for data_frame["Region_name"]
). 当我为每列分开运行autoARIMA时,这很好(例如,对于
data_frame["Region_name"]
)。
def __trainArima(self, actual_values, periods):
fitted_model = pm.auto_arima(actual_values, start_p=3,
start_q=2,
max_p=3, max_q=3, m=12,
start_P=0, seasonal=True,
d=1, D=1, trace=True,
error_action='ignore',
suppress_warnings=True,
stepwise=True)
return fitted_model.predict(n_periods=periods)
__trainArima(data_frame, 12)
However, I want to run it at once for all the 5 columns, having an output as a data frame of 5 columns for each region of predictions for the next 12 month. 但是,我想一次对所有5列都运行它,对于下一个12个月的每个预测区域,将其输出作为5列的数据框。 Is that possible?
那可能吗?
如果您不相信自己的列是相关的,则可以遍历每个列并运行ARIMA,然后合并预测。
To elaborate, you could loop though like this: 详细来说,您可以像这样循环:
import pandas as pd
results = []
cols = []
for i in data_frame.columns:
cols.append(i)
result = __trainArima(test[i])
results.append(result)
output = (pd.DataFrame(results)).T
output.columns=cols
that will output a data frame with your predictions under the same column names as they appear in the original dataframe. 它将输出一个数据框,其中的预测与原始数据框中显示的列名称相同。
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